Bioinformatics Infographic

The Future of Bioinformatics in Anesthesiology

What are Anesthesiologists?

Automated Algorithms can be utilized to determine postoperative care after anesthetic surgery

A novel algorithm was developed to calculate the mechanical duration of postoperative patients. This algorithm was studied on 238 UCLA cardiac cases and compared against STS data (Society for Thoracic Surgery: manually collected data). (1.)

Results of STS vs. Algorithm Calculated Data

More comprehensive

Algorithm proved to be:

Less computation time

More accurate

Closed loop systems vs STS

To interpret and utilize highly complex data sets, such as medical images, EEG waveforms, or multiple hemodynamic signals, over time

To analyze large amounts of data in order to search for reoccurring patterns or groups among variables (also known as data mining)

3 Common Goals of Anesthesiology

To generate models or algorithms to predict an event or continuous variable, such as degree of sedation, respiratory depression, or response to nociception

With the Application of Bioinformatics

Closed-loop devices and AI techniques within anesthesiology are used to improve patient care with the following goals:

• Keep patients within a physiologic target range.
• Reduce variability within an individual patient.

• Improve outcomes (Mortality rate)
• Reduce variability of care given to one patient versus another.

A subset of studies were conducted that were isolated to using BIS and total IV anesthesia

Closed-Loop Systems

In meta-analyses, the closed loop anesthetic delivery systems were associated with significant lower doses of propofol at induction of anesthesia and significantly shorter recovery time

and AI modalities have the potential to have an impact on the practice of Anesthesiology

Increased by

Increased by

12.3%

17.4%

The undershooting or overshooting of the proportion of time that was above or below the targeted set point in the manual groups compared with the closed loop groups

Time the depth of anesthesia was maintained in the desired range while using automated systems

Human Genetics and Genome Sequencing can Help Improve the Clinical Practice of Anesthesiologists

Anesthesia can be Individualized with an Emphasis on the Application of the Patient's Genetic Makeup

Whole-genome sequencing (WGS) analyzes a patient's entire genome.It can help identify genetic mutations and polymorphisms that can be linked to certain diseases.

WGS has brought upon a rapid growth of knowledge concerning inherited disease patterns and genetic polymorphisms that affect individual drug response.

WGS can be used to examine genetic causes of diseases associated with surgery, drugs or other interventions.

This will help provide Anesthesiologists with knowledge to aid in anesthetic plans and risks.

Association studies can be conducted and used to identify regions of polymorphism in DNA regions linked with disease.

Common genetic polymorphisms of anesthetic drugs that affect the day-to-day practice of the Anesthesiologist:

The inability of the metabolization and drug absorption of codeine affects up to 36% of patients. This causes reduction of the overall effectiveness of the drug.

Codeine

Multiple rare variant hypothesis

Decreases the rate of metabolism of morphine (creating more free drug), Decreases the rate of transport into the brain (decreasing drug availability to the receptor),Makes the receptor less sensitive to morphine (decreasing sensitivity).

In any single individual, a few relatively rare variants with each a relatively large effects, contribute to disease and can me true for many genes of drug metabolism.

Morphine

Atypical variant polymorphism of succinylcholine metabolism can cause patients to take longer to recover after surgery

Muscle Relaxant

Citations: [1] Gabel, E., Hofer, I. S., Satou, N., Grogan, T., Shemin, R., Mahajan, A., & Cannesson, M. (2017). Creation and Validation of an Automated Algorithm to Determine Postoperative Ventilator Requirements After Cardiac Surgery. Anesthesia and analgesia, 124(5), 1423–1430. https://doi.org/10.1213/ANE.0000000000001997 [2] Wingert, T., Lee, C., & Cannesson, M. (n.d.). Machine Learning, Deep Learning, and Closed Loop Devices-Anesthesia Delivery. Retrieved from https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9847584/ [3] Body, S. C. (n.d.). Genomics: Implications for anesthesia, perioperative care and outcomes. Retrieved from https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2868263/#:~:text=The%20studies%20are%20undertaken%20by,write%20this%20in%20mid%2D2009. [4] Galinkin, Jeffrey L. MD, FAAP*; Demmer, Laurie MD†; Yaster, Myron MD‡. Genetics for the Pediatric Anesthesiologist: A Primer on Congenital Malformations, Pharmacogenetics, and Proteomics. Anesthesia & Analgesia 111(5):p 1264-1274, https://journals.lww.com/anesthesia-analgesia/fulltext/2010/11000/Genetics_for_the_Pediatric_Anesthesiologist__A.28.aspx